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Open AccessArticle

To Blend or Not to Blend? A Framework for Nationwide Landsat–MODIS Data Selection for Crop Yield Prediction

1
CSIRO Data61, Goods Shed North, 34 Village St, Docklands, VIC 3008, Australia
2
CSIRO Land and Water, GPO Box 1700, Canberra, ACT 2061, Australia
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CSIRO Agriculture and Food, 306 Carmody Rd, St Lucia, QLD 4067, Australia
4
CSIRO Agriculture and Food, 147 Underwood Ave, Floreat, WA 6014, Australia
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Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(10), 1653; https://doi.org/10.3390/rs12101653
Received: 1 May 2020 / Revised: 19 May 2020 / Accepted: 19 May 2020 / Published: 21 May 2020
(This article belongs to the Special Issue Remote Sensing in Agriculture: State-of-the-Art)
The onus for monitoring crop growth from space is its ability to be applied anytime and anywhere, to produce crop yield estimates that are consistent at both the subfield scale for farming management strategies and the country level for national crop yield assessment. Historically, the requirements for satellites to successfully monitor crop growth and yield differed depending on the extent of the area being monitored. Diverging imaging capabilities can be reconciled by blending images from high-temporal-frequency (HTF) and high-spatial-resolution (HSR) sensors to produce images that possess both HTF and HSR characteristics across large areas. We evaluated the relative performance of Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat, and blended imagery for crop yield estimates (2009–2015) using a carbon-turnover yield model deployed across the Australian cropping area. Based on the fraction of missing Landsat observations, we further developed a parsimonious framework to inform when and where blending is beneficial for nationwide crop yield prediction at a finer scale (i.e., the 25-m pixel resolution). Landsat provided the best yield predictions when no observations were missing, which occurred in 17% of the cropping area of Australia. Blending was preferred when <42% of Landsat observations were missing, which occurred in 33% of the cropping area of Australia. MODIS produced a lower prediction error when ≥42% of the Landsat images were missing (~50% of the cropping area). By identifying when and where blending outperforms predictions from either Landsat or MODIS, the proposed framework enables more accurate monitoring of biophysical processes and yields, while keeping computational costs low. View Full-Text
Keywords: MODIS; Landsat; data blending; crop yield prediction; gap-filling MODIS; Landsat; data blending; crop yield prediction; gap-filling
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MDPI and ACS Style

Chen, Y.; McVicar, T.R.; Donohue, R.J.; Garg, N.; Waldner, F.; Ota, N.; Li, L.; Lawes, R. To Blend or Not to Blend? A Framework for Nationwide Landsat–MODIS Data Selection for Crop Yield Prediction. Remote Sens. 2020, 12, 1653.

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